Overview

Dataset statistics

Number of variables21
Number of observations4647
Missing cells542
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory615.0 B

Variable types

Categorical5
Numeric15
Boolean1

Alerts

specialization has a high cardinality: 358 distinct valuesHigh cardinality
specialization_en has a high cardinality: 172 distinct valuesHigh cardinality
city has a high cardinality: 807 distinct valuesHigh cardinality
specialization_id is highly overall correlated with country_code and 1 other fieldsHigh correlation
city_id is highly overall correlated with country_code and 1 other fieldsHigh correlation
fc_price is highly overall correlated with avg_priceHigh correlation
first_class_profiles is highly overall correlated with first_class_goal and 1 other fieldsHigh correlation
eligible_slots is highly overall correlated with premium and 6 other fieldsHigh correlation
premium is highly overall correlated with eligible_slots and 6 other fieldsHigh correlation
first_class_goal is highly overall correlated with first_class_profiles and 5 other fieldsHigh correlation
available_slots is highly overall correlated with eligible_slots and 2 other fieldsHigh correlation
traffic_x is highly overall correlated with user_bookingsHigh correlation
bookable_hours_month is highly overall correlated with eligible_slots and 4 other fieldsHigh correlation
active_calendars is highly overall correlated with first_class_profiles and 6 other fieldsHigh correlation
avg_price is highly overall correlated with fc_priceHigh correlation
admin_bookings is highly overall correlated with eligible_slots and 4 other fieldsHigh correlation
user_bookings is highly overall correlated with eligible_slots and 6 other fieldsHigh correlation
country_code is highly overall correlated with specialization_id and 2 other fieldsHigh correlation
first_class_limit is highly overall correlated with specialization_id and 2 other fieldsHigh correlation
median_active_months has 542 (11.7%) missing valuesMissing
first_class_profiles has 542 (11.7%) zerosZeros
eligible_slots has 81 (1.7%) zerosZeros
available_slots has 3180 (68.4%) zerosZeros
median_active_months has 199 (4.3%) zerosZeros
traffic_x has 48 (1.0%) zerosZeros
bookable_hours_month has 685 (14.7%) zerosZeros
active_calendars has 663 (14.3%) zerosZeros
avg_price has 827 (17.8%) zerosZeros
admin_bookings has 785 (16.9%) zerosZeros
user_bookings has 648 (13.9%) zerosZeros

Reproduction

Analysis started2023-09-21 18:03:00.861740
Analysis finished2023-09-21 18:06:51.241649
Duration3 minutes and 50.38 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

country_code
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size304.1 KiB
mx
1482 
br
1038 
pl
648 
it
600 
es
341 
Other values (4)
538 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9294
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowar
2nd rowar
3rd rowar
4th rowar
5th rowar

Common Values

ValueCountFrequency (%)
mx 1482
31.9%
br 1038
22.3%
pl 648
13.9%
it 600
12.9%
es 341
 
7.3%
de 187
 
4.0%
co 129
 
2.8%
cl 121
 
2.6%
ar 101
 
2.2%

Length

2023-09-21T18:06:51.463945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T18:06:51.942383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mx 1482
31.9%
br 1038
22.3%
pl 648
13.9%
it 600
12.9%
es 341
 
7.3%
de 187
 
4.0%
co 129
 
2.8%
cl 121
 
2.6%
ar 101
 
2.2%

Most occurring characters

ValueCountFrequency (%)
m 1482
15.9%
x 1482
15.9%
r 1139
12.3%
b 1038
11.2%
l 769
8.3%
p 648
7.0%
i 600
6.5%
t 600
6.5%
e 528
 
5.7%
s 341
 
3.7%
Other values (4) 667
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9294
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1482
15.9%
x 1482
15.9%
r 1139
12.3%
b 1038
11.2%
l 769
8.3%
p 648
7.0%
i 600
6.5%
t 600
6.5%
e 528
 
5.7%
s 341
 
3.7%
Other values (4) 667
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 9294
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1482
15.9%
x 1482
15.9%
r 1139
12.3%
b 1038
11.2%
l 769
8.3%
p 648
7.0%
i 600
6.5%
t 600
6.5%
e 528
 
5.7%
s 341
 
3.7%
Other values (4) 667
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1482
15.9%
x 1482
15.9%
r 1139
12.3%
b 1038
11.2%
l 769
8.3%
p 648
7.0%
i 600
6.5%
t 600
6.5%
e 528
 
5.7%
s 341
 
3.7%
Other values (4) 667
7.2%

specialization_id
Real number (ℝ)

Distinct157
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.637831
Minimum1
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:52.363287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q130
median60
Q378
95-th percentile125
Maximum191
Range190
Interquartile range (IQR)48

Descriptive statistics

Standard deviation35.303327
Coefficient of variation (CV)0.60205718
Kurtosis0.62022034
Mean58.637831
Median Absolute Deviation (MAD)24
Skewness0.69417506
Sum272490
Variance1246.3249
MonotonicityNot monotonic
2023-09-21T18:06:52.855014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 171
 
3.7%
78 169
 
3.6%
12 145
 
3.1%
60 145
 
3.1%
93 135
 
2.9%
63 123
 
2.6%
64 110
 
2.4%
30 107
 
2.3%
69 100
 
2.2%
71 90
 
1.9%
Other values (147) 3352
72.1%
ValueCountFrequency (%)
1 8
 
0.2%
2 34
0.7%
3 5
 
0.1%
4 13
 
0.3%
5 32
0.7%
6 21
 
0.5%
7 76
1.6%
8 48
1.0%
9 3
 
0.1%
10 38
0.8%
ValueCountFrequency (%)
191 2
 
< 0.1%
190 2
 
< 0.1%
184 3
0.1%
182 1
 
< 0.1%
180 5
0.1%
177 1
 
< 0.1%
176 6
0.1%
175 1
 
< 0.1%
174 2
 
< 0.1%
172 1
 
< 0.1%

city_id
Real number (ℝ)

Distinct762
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3303.1364
Minimum4
Maximum31887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:53.438480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile82
Q1420
median1057
Q33112
95-th percentile18417
Maximum31887
Range31883
Interquartile range (IQR)2692

Descriptive statistics

Standard deviation5940.6844
Coefficient of variation (CV)1.7984981
Kurtosis7.7110262
Mean3303.1364
Median Absolute Deviation (MAD)802
Skewness2.8496111
Sum15349675
Variance35291732
MonotonicityNot monotonic
2023-09-21T18:06:53.950318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1610 69
 
1.5%
162 69
 
1.5%
5228 68
 
1.5%
22535 66
 
1.4%
824 64
 
1.4%
3650 64
 
1.4%
1688 55
 
1.2%
15469 53
 
1.1%
626 52
 
1.1%
1623 48
 
1.0%
Other values (752) 4039
86.9%
ValueCountFrequency (%)
4 9
 
0.2%
8 3
 
0.1%
9 13
 
0.3%
11 3
 
0.1%
13 1
 
< 0.1%
14 5
 
0.1%
16 6
 
0.1%
19 38
0.8%
20 23
0.5%
22 2
 
< 0.1%
ValueCountFrequency (%)
31887 3
 
0.1%
31363 2
 
< 0.1%
31297 6
0.1%
30499 1
 
< 0.1%
30387 1
 
< 0.1%
30255 11
0.2%
29983 4
 
0.1%
29167 3
 
0.1%
29041 2
 
< 0.1%
28653 1
 
< 0.1%

specialization
Categorical

Distinct358
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size403.8 KiB
Psicólogo
 
352
Psiquiatra
 
152
Ginecólogo
 
121
Urólogo
 
108
Traumatólogo
 
82
Other values (353)
3832 

Length

Max length52
Median length42
Mean length13.054444
Min length6

Characters and Unicode

Total characters60664
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)1.7%

Sample

1st rowUrólogo
2nd rowMédico clínico
3rd rowPsicólogo
4th rowNeurocirujano
5th rowPsicólogo

Common Values

ValueCountFrequency (%)
Psicólogo 352
 
7.6%
Psiquiatra 152
 
3.3%
Ginecólogo 121
 
2.6%
Urólogo 108
 
2.3%
Traumatólogo 82
 
1.8%
Pediatra 81
 
1.7%
Cirujano general 81
 
1.7%
Oftalmólogo 79
 
1.7%
Dentista 78
 
1.7%
Dentista - Odontólogo 70
 
1.5%
Other values (348) 3443
74.1%

Length

2023-09-21T18:06:54.279033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
psicólogo 363
 
5.7%
cirujano 184
 
2.9%
dentista 173
 
2.7%
psiquiatra 155
 
2.4%
144
 
2.3%
médico 142
 
2.2%
cirurgião 137
 
2.2%
general 133
 
2.1%
ginecólogo 127
 
2.0%
pediatra 119
 
1.9%
Other values (338) 4653
73.5%

Most occurring characters

ValueCountFrequency (%)
o 8259
13.6%
i 5328
 
8.8%
a 4770
 
7.9%
t 4245
 
7.0%
r 4106
 
6.8%
l 3775
 
6.2%
g 3487
 
5.7%
e 3465
 
5.7%
s 2669
 
4.4%
c 2529
 
4.2%
Other values (56) 18031
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54972
90.6%
Uppercase Letter 3764
 
6.2%
Space Separator 1683
 
2.8%
Dash Punctuation 172
 
0.3%
Other Punctuation 31
 
0.1%
Open Punctuation 21
 
< 0.1%
Close Punctuation 21
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8259
15.0%
i 5328
9.7%
a 4770
 
8.7%
t 4245
 
7.7%
r 4106
 
7.5%
l 3775
 
6.9%
g 3487
 
6.3%
e 3465
 
6.3%
s 2669
 
4.9%
c 2529
 
4.6%
Other values (28) 12339
22.4%
Uppercase Letter
ValueCountFrequency (%)
P 729
19.4%
O 489
13.0%
C 446
11.8%
N 316
8.4%
D 311
8.3%
G 274
 
7.3%
U 190
 
5.0%
M 166
 
4.4%
T 155
 
4.1%
E 145
 
3.9%
Other values (12) 543
14.4%
Other Punctuation
ValueCountFrequency (%)
& 30
96.8%
, 1
 
3.2%
Space Separator
ValueCountFrequency (%)
1683
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58736
96.8%
Common 1928
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8259
14.1%
i 5328
 
9.1%
a 4770
 
8.1%
t 4245
 
7.2%
r 4106
 
7.0%
l 3775
 
6.4%
g 3487
 
5.9%
e 3465
 
5.9%
s 2669
 
4.5%
c 2529
 
4.3%
Other values (50) 16103
27.4%
Common
ValueCountFrequency (%)
1683
87.3%
- 172
 
8.9%
& 30
 
1.6%
( 21
 
1.1%
) 21
 
1.1%
, 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58391
96.3%
None 2273
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8259
14.1%
i 5328
 
9.1%
a 4770
 
8.2%
t 4245
 
7.3%
r 4106
 
7.0%
l 3775
 
6.5%
g 3487
 
6.0%
e 3465
 
5.9%
s 2669
 
4.6%
c 2529
 
4.3%
Other values (43) 15758
27.0%
None
ValueCountFrequency (%)
ó 1557
68.5%
é 183
 
8.1%
ã 180
 
7.9%
á 123
 
5.4%
í 79
 
3.5%
ę 53
 
2.3%
ä 25
 
1.1%
ç 24
 
1.1%
ą 18
 
0.8%
ü 14
 
0.6%
Other values (3) 17
 
0.7%
Distinct172
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size359.1 KiB
psychologist
467 
dentist
 
279
gynecologist
 
229
urologist
 
205
psychiatrist
 
182
Other values (167)
3285 

Length

Max length50
Median length42
Mean length14.121369
Min length6

Characters and Unicode

Total characters65622
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.6%

Sample

1st rowurologist
2nd rowclinician
3rd rowpsychologist
4th rowneurosurgeon
5th rowpsychologist

Common Values

ValueCountFrequency (%)
psychologist 467
 
10.0%
dentist 279
 
6.0%
gynecologist 229
 
4.9%
urologist 205
 
4.4%
psychiatrist 182
 
3.9%
ophthalmologist 138
 
3.0%
dermatologist 129
 
2.8%
general surgeon 127
 
2.7%
orthopedist 117
 
2.5%
cardiologist 114
 
2.5%
Other values (162) 2660
57.2%

Length

2023-09-21T18:06:54.591130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
surgeon 530
 
8.5%
psychologist 503
 
8.0%
dentist 289
 
4.6%
gynecologist 229
 
3.7%
general 223
 
3.6%
urologist 209
 
3.3%
psychiatrist 188
 
3.0%
orthopedist 169
 
2.7%
specialist 151
 
2.4%
ophthalmologist 142
 
2.3%
Other values (153) 3617
57.9%

Most occurring characters

ValueCountFrequency (%)
o 7646
11.7%
t 7113
10.8%
i 6984
10.6%
s 6027
9.2%
e 4313
 
6.6%
r 4108
 
6.3%
g 3769
 
5.7%
l 3672
 
5.6%
n 3670
 
5.6%
a 3485
 
5.3%
Other values (16) 14835
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64005
97.5%
Space Separator 1616
 
2.5%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 7646
11.9%
t 7113
11.1%
i 6984
10.9%
s 6027
9.4%
e 4313
 
6.7%
r 4108
 
6.4%
g 3769
 
5.9%
l 3672
 
5.7%
n 3670
 
5.7%
a 3485
 
5.4%
Other values (14) 13218
20.7%
Space Separator
ValueCountFrequency (%)
1616
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64005
97.5%
Common 1617
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 7646
11.9%
t 7113
11.1%
i 6984
10.9%
s 6027
9.4%
e 4313
 
6.7%
r 4108
 
6.4%
g 3769
 
5.9%
l 3672
 
5.7%
n 3670
 
5.7%
a 3485
 
5.4%
Other values (14) 13218
20.7%
Common
ValueCountFrequency (%)
1616
99.9%
, 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 7646
11.7%
t 7113
10.8%
i 6984
10.6%
s 6027
9.2%
e 4313
 
6.6%
r 4108
 
6.3%
g 3769
 
5.7%
l 3672
 
5.6%
n 3670
 
5.6%
a 3485
 
5.3%
Other values (16) 14835
22.6%

city
Categorical

Distinct807
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size374.0 KiB
Ciudad de México
 
69
Warszawa
 
69
São Paulo
 
68
Roma
 
66
Rio de Janeiro
 
64
Other values (802)
4311 

Length

Max length26
Median length23
Mean length9.4540564
Min length3

Characters and Unicode

Total characters43933
Distinct characters84
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique362 ?
Unique (%)7.8%

Sample

1st rowNeuquén Capital
2nd rowMendoza Capital
3rd rowVicente López
4th rowMendoza Capital
5th rowNeuquén Capital

Common Values

ValueCountFrequency (%)
Ciudad de México 69
 
1.5%
Warszawa 69
 
1.5%
São Paulo 68
 
1.5%
Roma 66
 
1.4%
Rio de Janeiro 64
 
1.4%
Wrocław 55
 
1.2%
Milano 53
 
1.1%
Kraków 52
 
1.1%
Cuauhtémoc 49
 
1.1%
Belo Horizonte 48
 
1.0%
Other values (797) 4054
87.2%

Length

2023-09-21T18:06:54.915437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 362
 
5.5%
são 125
 
1.9%
ciudad 101
 
1.5%
san 95
 
1.4%
rio 90
 
1.4%
juárez 80
 
1.2%
méxico 69
 
1.0%
warszawa 69
 
1.0%
paulo 68
 
1.0%
roma 66
 
1.0%
Other values (930) 5485
83.0%

Most occurring characters

ValueCountFrequency (%)
a 6153
 
14.0%
o 3519
 
8.0%
e 3140
 
7.1%
i 2715
 
6.2%
r 2690
 
6.1%
l 2350
 
5.3%
n 2190
 
5.0%
1963
 
4.5%
u 1658
 
3.8%
d 1484
 
3.4%
Other values (74) 16071
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35806
81.5%
Uppercase Letter 6136
 
14.0%
Space Separator 1963
 
4.5%
Other Punctuation 15
 
< 0.1%
Dash Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6153
17.2%
o 3519
9.8%
e 3140
 
8.8%
i 2715
 
7.6%
r 2690
 
7.5%
l 2350
 
6.6%
n 2190
 
6.1%
u 1658
 
4.6%
d 1484
 
4.1%
t 1409
 
3.9%
Other values (39) 8498
23.7%
Uppercase Letter
ValueCountFrequency (%)
C 780
12.7%
M 611
 
10.0%
S 555
 
9.0%
P 524
 
8.5%
B 499
 
8.1%
G 393
 
6.4%
T 328
 
5.3%
R 283
 
4.6%
J 265
 
4.3%
L 232
 
3.8%
Other values (21) 1666
27.2%
Other Punctuation
ValueCountFrequency (%)
, 9
60.0%
' 6
40.0%
Space Separator
ValueCountFrequency (%)
1963
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41942
95.5%
Common 1991
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6153
14.7%
o 3519
 
8.4%
e 3140
 
7.5%
i 2715
 
6.5%
r 2690
 
6.4%
l 2350
 
5.6%
n 2190
 
5.2%
u 1658
 
4.0%
d 1484
 
3.5%
t 1409
 
3.4%
Other values (70) 14634
34.9%
Common
ValueCountFrequency (%)
1963
98.6%
- 13
 
0.7%
, 9
 
0.5%
' 6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42313
96.3%
None 1620
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6153
14.5%
o 3519
 
8.3%
e 3140
 
7.4%
i 2715
 
6.4%
r 2690
 
6.4%
l 2350
 
5.6%
n 2190
 
5.2%
1963
 
4.6%
u 1658
 
3.9%
d 1484
 
3.5%
Other values (46) 14451
34.2%
None
ValueCountFrequency (%)
ó 328
20.2%
é 280
17.3%
á 237
14.6%
ã 154
9.5%
í 110
 
6.8%
ł 102
 
6.3%
ń 93
 
5.7%
â 51
 
3.1%
ü 48
 
3.0%
Ł 43
 
2.7%
Other values (18) 174
10.7%

fc_price
Real number (ℝ)

Distinct57
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4311.5293
Minimum49
Maximum504201.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:55.189780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile49
Q1195.02
median199
Q3999
95-th percentile24369.75
Maximum504201.68
Range504152.68
Interquartile range (IQR)803.98

Descriptive statistics

Standard deviation21045.214
Coefficient of variation (CV)4.8811483
Kurtosis176.34163
Mean4311.5293
Median Absolute Deviation (MAD)150
Skewness11.23748
Sum20035677
Variance4.4290103 × 108
MonotonicityNot monotonic
2023-09-21T18:06:55.496239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999 1176
25.3%
195.02 825
17.8%
79 469
 
10.1%
199 408
 
8.8%
49 271
 
5.8%
99 246
 
5.3%
1599 162
 
3.5%
299 151
 
3.2%
67226.89 108
 
2.3%
391.02 106
 
2.3%
Other values (47) 725
15.6%
ValueCountFrequency (%)
49 271
 
5.8%
69 29
 
0.6%
79 469
10.1%
99 246
 
5.3%
129 44
 
0.9%
149 24
 
0.5%
169 19
 
0.4%
195.02 825
17.8%
199 408
8.8%
219 1
 
< 0.1%
ValueCountFrequency (%)
504201.68 1
 
< 0.1%
378151.26 3
 
0.1%
252100.84 10
 
0.2%
134453.78 2
 
< 0.1%
126050.42 7
 
0.2%
100000 6
 
0.1%
74789.92 5
 
0.1%
67226.89 108
2.3%
49579.83 12
 
0.3%
24369.75 96
2.1%

is_listed
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.8 KiB
True
2579 
False
2068 
ValueCountFrequency (%)
True 2579
55.5%
False 2068
44.5%
2023-09-21T18:06:55.794108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

first_class_profiles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1542931
Minimum0
Maximum16
Zeros542
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:55.982623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile7
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3050027
Coefficient of variation (CV)1.0699578
Kurtosis6.1631635
Mean2.1542931
Median Absolute Deviation (MAD)1
Skewness2.259911
Sum10011
Variance5.3130374
MonotonicityNot monotonic
2023-09-21T18:06:56.225375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 2156
46.4%
2 697
 
15.0%
0 542
 
11.7%
3 404
 
8.7%
4 265
 
5.7%
5 165
 
3.6%
6 144
 
3.1%
7 79
 
1.7%
8 61
 
1.3%
9 36
 
0.8%
Other values (7) 98
 
2.1%
ValueCountFrequency (%)
0 542
 
11.7%
1 2156
46.4%
2 697
 
15.0%
3 404
 
8.7%
4 265
 
5.7%
5 165
 
3.6%
6 144
 
3.1%
7 79
 
1.7%
8 61
 
1.3%
9 36
 
0.8%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 10
 
0.2%
14 6
 
0.1%
13 7
 
0.2%
12 16
 
0.3%
11 22
 
0.5%
10 36
0.8%
9 36
0.8%
8 61
1.3%
7 79
1.7%

eligible_slots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct172
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.607274
Minimum0
Maximum649
Zeros81
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:56.511859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q317
95-th percentile60
Maximum649
Range649
Interquartile range (IQR)14

Descriptive statistics

Standard deviation32.626502
Coefficient of variation (CV)1.9645911
Kurtosis80.156677
Mean16.607274
Median Absolute Deviation (MAD)6
Skewness7.1228848
Sum77174
Variance1064.4887
MonotonicityNot monotonic
2023-09-21T18:06:56.817955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 604
 
13.0%
2 384
 
8.3%
3 331
 
7.1%
5 255
 
5.5%
4 241
 
5.2%
6 211
 
4.5%
9 171
 
3.7%
7 168
 
3.6%
8 164
 
3.5%
10 153
 
3.3%
Other values (162) 1965
42.3%
ValueCountFrequency (%)
0 81
 
1.7%
1 604
13.0%
2 384
8.3%
3 331
7.1%
4 241
 
5.2%
5 255
5.5%
6 211
 
4.5%
7 168
 
3.6%
8 164
 
3.5%
9 171
 
3.7%
ValueCountFrequency (%)
649 1
< 0.1%
519 1
< 0.1%
481 1
< 0.1%
398 1
< 0.1%
390 1
< 0.1%
383 1
< 0.1%
368 1
< 0.1%
337 1
< 0.1%
304 1
< 0.1%
285 1
< 0.1%

premium
Real number (ℝ)

Distinct215
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.98838
Minimum1
Maximum873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:57.116495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median11
Q324
95-th percentile80
Maximum873
Range872
Interquartile range (IQR)20

Descriptive statistics

Standard deviation43.508059
Coefficient of variation (CV)1.892611
Kurtosis72.30696
Mean22.98838
Median Absolute Deviation (MAD)8
Skewness6.7273811
Sum106827
Variance1892.9512
MonotonicityNot monotonic
2023-09-21T18:06:57.406501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 507
 
10.9%
3 285
 
6.1%
2 272
 
5.9%
4 242
 
5.2%
5 191
 
4.1%
7 173
 
3.7%
6 170
 
3.7%
8 146
 
3.1%
15 141
 
3.0%
9 133
 
2.9%
Other values (205) 2387
51.4%
ValueCountFrequency (%)
1 507
10.9%
2 272
5.9%
3 285
6.1%
4 242
5.2%
5 191
 
4.1%
6 170
 
3.7%
7 173
 
3.7%
8 146
 
3.1%
9 133
 
2.9%
10 118
 
2.5%
ValueCountFrequency (%)
873 1
< 0.1%
653 1
< 0.1%
578 1
< 0.1%
467 1
< 0.1%
463 1
< 0.1%
461 1
< 0.1%
449 1
< 0.1%
446 1
< 0.1%
436 1
< 0.1%
408 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
15.0
2627 
10.0
2020 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters18588
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
15.0 2627
56.5%
10.0 2020
43.5%

Length

2023-09-21T18:06:57.671385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T18:06:57.920995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15.0 2627
56.5%
10.0 2020
43.5%

Most occurring characters

ValueCountFrequency (%)
0 6667
35.9%
1 4647
25.0%
. 4647
25.0%
5 2627
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13941
75.0%
Other Punctuation 4647
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6667
47.8%
1 4647
33.3%
5 2627
 
18.8%
Other Punctuation
ValueCountFrequency (%)
. 4647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18588
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6667
35.9%
1 4647
25.0%
. 4647
25.0%
5 2627
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6667
35.9%
1 4647
25.0%
. 4647
25.0%
5 2627
 
14.1%

first_class_goal
Real number (ℝ)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9752529
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:58.103387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3698512
Coefficient of variation (CV)1.0992637
Kurtosis0.87476263
Mean3.9752529
Median Absolute Deviation (MAD)1
Skewness1.4772094
Sum18473
Variance19.095599
MonotonicityNot monotonic
2023-09-21T18:06:58.324645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 2187
47.1%
2 586
 
12.6%
4 348
 
7.5%
15 346
 
7.4%
10 273
 
5.9%
5 271
 
5.8%
3 244
 
5.3%
9 95
 
2.0%
6 87
 
1.9%
11 59
 
1.3%
Other values (5) 151
 
3.2%
ValueCountFrequency (%)
1 2187
47.1%
2 586
 
12.6%
3 244
 
5.3%
4 348
 
7.5%
5 271
 
5.8%
6 87
 
1.9%
7 32
 
0.7%
8 14
 
0.3%
9 95
 
2.0%
10 273
 
5.9%
ValueCountFrequency (%)
15 346
7.4%
14 29
 
0.6%
13 35
 
0.8%
12 41
 
0.9%
11 59
 
1.3%
10 273
5.9%
9 95
 
2.0%
8 14
 
0.3%
7 32
 
0.7%
6 87
 
1.9%

available_slots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8287067
Minimum0
Maximum15
Zeros3180
Zeros (%)68.4%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:58.573280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation3.532935
Coefficient of variation (CV)1.9319309
Kurtosis2.9122541
Mean1.8287067
Median Absolute Deviation (MAD)0
Skewness1.994463
Sum8498
Variance12.481629
MonotonicityNot monotonic
2023-09-21T18:06:58.800574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 3180
68.4%
1 305
 
6.6%
4 168
 
3.6%
10 115
 
2.5%
3 114
 
2.5%
2 112
 
2.4%
9 111
 
2.4%
5 105
 
2.3%
7 81
 
1.7%
6 75
 
1.6%
Other values (6) 281
 
6.0%
ValueCountFrequency (%)
0 3180
68.4%
1 305
 
6.6%
2 112
 
2.4%
3 114
 
2.5%
4 168
 
3.6%
5 105
 
2.3%
6 75
 
1.6%
7 81
 
1.7%
8 57
 
1.2%
9 111
 
2.4%
ValueCountFrequency (%)
15 34
 
0.7%
14 20
 
0.4%
13 43
 
0.9%
12 62
1.3%
11 65
1.4%
10 115
2.5%
9 111
2.4%
8 57
1.2%
7 81
1.7%
6 75
1.6%

median_active_months
Real number (ℝ)

MISSING  ZEROS 

Distinct118
Distinct (%)2.9%
Missing542
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean19.062607
Minimum0
Maximum69
Zeros199
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:59.083506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q16
median14
Q328
95-th percentile54
Maximum69
Range69
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.359303
Coefficient of variation (CV)0.85818816
Kurtosis-0.12334574
Mean19.062607
Median Absolute Deviation (MAD)9
Skewness0.95446128
Sum78252
Variance267.62681
MonotonicityNot monotonic
2023-09-21T18:06:59.390487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 199
 
4.3%
10 156
 
3.4%
2 148
 
3.2%
6 143
 
3.1%
54 141
 
3.0%
3 137
 
2.9%
8 121
 
2.6%
7 118
 
2.5%
9 116
 
2.5%
14 112
 
2.4%
Other values (108) 2714
58.4%
(Missing) 542
 
11.7%
ValueCountFrequency (%)
0 199
4.3%
0.5 7
 
0.2%
1 109
2.3%
1.5 5
 
0.1%
2 148
3.2%
2.5 13
 
0.3%
3 137
2.9%
3.5 24
 
0.5%
4 108
2.3%
4.5 16
 
0.3%
ValueCountFrequency (%)
69 1
 
< 0.1%
68 1
 
< 0.1%
59.5 1
 
< 0.1%
59 61
1.3%
58 5
 
0.1%
57.5 2
 
< 0.1%
57 3
 
0.1%
56 55
 
1.2%
55 7
 
0.2%
54 141
3.0%

traffic_x
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1855
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean939.41166
Minimum0
Maximum30988
Zeros48
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:06:59.703752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q1126
median394
Q31051
95-th percentile3740.3
Maximum30988
Range30988
Interquartile range (IQR)925

Descriptive statistics

Standard deviation1688.225
Coefficient of variation (CV)1.7971088
Kurtosis57.56996
Mean939.41166
Median Absolute Deviation (MAD)322
Skewness5.8006057
Sum4365446
Variance2850103.6
MonotonicityNot monotonic
2023-09-21T18:06:59.998083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
1.0%
20 31
 
0.7%
6 28
 
0.6%
12 28
 
0.6%
4 26
 
0.6%
24 26
 
0.6%
30 22
 
0.5%
8 20
 
0.4%
18 20
 
0.4%
32 20
 
0.4%
Other values (1845) 4378
94.2%
ValueCountFrequency (%)
0 48
1.0%
1 2
 
< 0.1%
2 18
 
0.4%
3 3
 
0.1%
4 26
0.6%
5 7
 
0.2%
6 28
0.6%
7 2
 
< 0.1%
8 20
0.4%
9 3
 
0.1%
ValueCountFrequency (%)
30988 1
< 0.1%
24028 1
< 0.1%
21900 1
< 0.1%
21730 1
< 0.1%
20072 1
< 0.1%
18164 1
< 0.1%
17568 1
< 0.1%
17285 1
< 0.1%
15100 1
< 0.1%
14234 1
< 0.1%

bookable_hours_month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1857
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1201.9884
Minimum0
Maximum90136
Zeros685
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:07:00.306537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156
median298
Q31018
95-th percentile4746
Maximum90136
Range90136
Interquartile range (IQR)962

Descriptive statistics

Standard deviation3545.5071
Coefficient of variation (CV)2.9497016
Kurtosis179.63769
Mean1201.9884
Median Absolute Deviation (MAD)297
Skewness10.741296
Sum5585640
Variance12570621
MonotonicityNot monotonic
2023-09-21T18:07:00.594106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 685
 
14.7%
2 16
 
0.3%
1 14
 
0.3%
122 13
 
0.3%
75 13
 
0.3%
6 13
 
0.3%
4 13
 
0.3%
22 13
 
0.3%
87 12
 
0.3%
21 12
 
0.3%
Other values (1847) 3843
82.7%
ValueCountFrequency (%)
0 685
14.7%
1 14
 
0.3%
2 16
 
0.3%
3 10
 
0.2%
4 13
 
0.3%
5 12
 
0.3%
6 13
 
0.3%
7 6
 
0.1%
8 9
 
0.2%
9 7
 
0.2%
ValueCountFrequency (%)
90136 1
< 0.1%
71670 1
< 0.1%
64858 1
< 0.1%
54226 1
< 0.1%
45741 1
< 0.1%
43588 1
< 0.1%
42360 1
< 0.1%
36284 1
< 0.1%
36170 1
< 0.1%
34948 1
< 0.1%

active_calendars
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct221
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.185066
Minimum0
Maximum1096
Zeros663
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:07:00.911870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q320
95-th percentile79
Maximum1096
Range1096
Interquartile range (IQR)18

Descriptive statistics

Standard deviation51.008058
Coefficient of variation (CV)2.4077366
Kurtosis119.91957
Mean21.185066
Median Absolute Deviation (MAD)7
Skewness8.7840952
Sum98447
Variance2601.822
MonotonicityNot monotonic
2023-09-21T18:07:01.204759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 663
 
14.3%
2 339
 
7.3%
3 268
 
5.8%
1 253
 
5.4%
4 217
 
4.7%
7 178
 
3.8%
5 175
 
3.8%
6 165
 
3.6%
8 153
 
3.3%
11 129
 
2.8%
Other values (211) 2107
45.3%
ValueCountFrequency (%)
0 663
14.3%
1 253
 
5.4%
2 339
7.3%
3 268
5.8%
4 217
 
4.7%
5 175
 
3.8%
6 165
 
3.6%
7 178
 
3.8%
8 153
 
3.3%
9 122
 
2.6%
ValueCountFrequency (%)
1096 1
< 0.1%
1001 1
< 0.1%
788 1
< 0.1%
715 1
< 0.1%
684 1
< 0.1%
645 1
< 0.1%
571 1
< 0.1%
569 1
< 0.1%
537 1
< 0.1%
476 1
< 0.1%

avg_price
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1236
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3727.875
Minimum0
Maximum189473
Zeros827
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:07:01.501547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q166
median237
Q3685
95-th percentile1571.7
Maximum189473
Range189473
Interquartile range (IQR)619

Descriptive statistics

Standard deviation18948.799
Coefficient of variation (CV)5.083003
Kurtosis43.74238
Mean3727.875
Median Absolute Deviation (MAD)198
Skewness6.4913164
Sum17323435
Variance3.59057 × 108
MonotonicityNot monotonic
2023-09-21T18:07:01.800705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 827
 
17.8%
58 24
 
0.5%
56 22
 
0.5%
250 20
 
0.4%
500 20
 
0.4%
62 19
 
0.4%
265 19
 
0.4%
275 19
 
0.4%
67 19
 
0.4%
53 19
 
0.4%
Other values (1226) 3639
78.3%
ValueCountFrequency (%)
0 827
17.8%
2 1
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
19 3
 
0.1%
ValueCountFrequency (%)
189473 1
< 0.1%
188070 1
< 0.1%
180000 1
< 0.1%
171916 1
< 0.1%
170875 1
< 0.1%
169557 1
< 0.1%
163636 1
< 0.1%
162500 1
< 0.1%
162065 1
< 0.1%
160000 1
< 0.1%

admin_bookings
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1505
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean764.32343
Minimum0
Maximum82873
Zeros785
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:07:03.751829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125
median196
Q3629
95-th percentile2815.5
Maximum82873
Range82873
Interquartile range (IQR)604

Descriptive statistics

Standard deviation2556.0134
Coefficient of variation (CV)3.3441515
Kurtosis329.16325
Mean764.32343
Median Absolute Deviation (MAD)196
Skewness14.37708
Sum3551811
Variance6533204.4
MonotonicityNot monotonic
2023-09-21T18:07:04.136845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 785
 
16.9%
1 42
 
0.9%
2 20
 
0.4%
7 20
 
0.4%
9 20
 
0.4%
25 19
 
0.4%
10 18
 
0.4%
3 17
 
0.4%
6 16
 
0.3%
12 16
 
0.3%
Other values (1495) 3674
79.1%
ValueCountFrequency (%)
0 785
16.9%
1 42
 
0.9%
2 20
 
0.4%
3 17
 
0.4%
4 16
 
0.3%
5 16
 
0.3%
6 16
 
0.3%
7 20
 
0.4%
8 16
 
0.3%
9 20
 
0.4%
ValueCountFrequency (%)
82873 1
< 0.1%
56173 1
< 0.1%
44331 1
< 0.1%
35028 1
< 0.1%
30766 1
< 0.1%
30660 1
< 0.1%
30083 1
< 0.1%
29745 1
< 0.1%
25769 1
< 0.1%
24634 1
< 0.1%

user_bookings
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct727
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.93673
Minimum0
Maximum12297
Zeros648
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size72.6 KiB
2023-09-21T18:07:04.537761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median37
Q3134
95-th percentile726.7
Maximum12297
Range12297
Interquartile range (IQR)127

Descriptive statistics

Standard deviation544.3772
Coefficient of variation (CV)3.0941645
Kurtosis135.03852
Mean175.93673
Median Absolute Deviation (MAD)37
Skewness9.6771495
Sum817578
Variance296346.54
MonotonicityNot monotonic
2023-09-21T18:07:04.959762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 648
 
13.9%
1 109
 
2.3%
2 90
 
1.9%
3 80
 
1.7%
5 77
 
1.7%
6 70
 
1.5%
4 66
 
1.4%
11 64
 
1.4%
7 64
 
1.4%
10 58
 
1.2%
Other values (717) 3321
71.5%
ValueCountFrequency (%)
0 648
13.9%
1 109
 
2.3%
2 90
 
1.9%
3 80
 
1.7%
4 66
 
1.4%
5 77
 
1.7%
6 70
 
1.5%
7 64
 
1.4%
8 58
 
1.2%
9 41
 
0.9%
ValueCountFrequency (%)
12297 1
< 0.1%
9861 1
< 0.1%
8506 1
< 0.1%
8189 1
< 0.1%
7156 1
< 0.1%
6853 1
< 0.1%
6835 1
< 0.1%
6268 1
< 0.1%
6247 1
< 0.1%
6025 1
< 0.1%

Interactions

2023-09-21T18:06:39.302637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:07.251295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:26.723775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:26.147938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:37.187617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:47.543372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:58.619852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:09.578423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:21.050919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:31.556570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:42.455640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:53.695891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:03.982971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:15.980267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:27.544855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:40.474440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:08.913732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:30.349242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:27.239938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:38.419491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:49.265481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:59.720183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:10.673111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:22.921396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:32.712370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:43.608313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:54.829958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:07.393581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:17.233519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:28.706489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:45.626271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:14.572880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:39.754868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:32.168732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:44.174982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:55.229720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:05.557299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:16.514301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:27.955578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:39.044347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:48.888562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:00.530389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:12.587631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:24.007514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:34.517120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:45.872852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:15.327764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:42.542919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:32.528064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:44.427829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:55.495578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:05.948838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:16.771327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:28.220565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:39.291291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:49.251187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:00.779507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:12.841868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:24.264100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:34.947743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:46.146840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:16.080679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:45.899930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:32.904913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:44.691441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:55.781004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:06.363129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:17.055624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:28.529599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:39.575336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:49.657701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:01.028943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:13.112851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:24.566566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:35.328803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:46.425007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:17.195379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:49.629798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:33.273852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:44.960538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:56.040423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:06.772471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:17.320762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:28.800206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:39.838732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:50.047099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:01.279691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:13.360244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:24.834591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:35.704922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:46.682940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:18.317139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:53.392742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:33.608376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:45.202074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:56.286453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:07.141069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:17.576068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:29.056206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:40.084821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:50.420400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:01.524680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:13.603152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:25.082142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:36.113349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:46.947757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:19.404380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:56.227738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:34.012348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:45.471945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:56.554132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:07.553821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:17.953911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:29.323973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:40.339899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:50.826077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:01.796339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:13.860307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:25.350263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:36.513027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:47.212528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:20.535746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:59.626707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:34.434827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:45.726597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:56.808873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:07.813540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:18.372449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:29.615683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:40.615458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:51.168547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:02.036956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:14.127685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:25.640589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:36.936306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:47.497728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:21.682605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:02.529054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:34.792025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:45.993226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:57.062177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:08.071716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:18.798298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:29.895752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:40.879129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:51.595371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:02.307297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:14.410066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:25.912857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:37.383307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:47.752975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:22.603839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:06.870822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:35.185214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:46.248292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:57.322876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:08.332679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:19.195592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:30.165328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:41.140224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:52.012014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:02.559461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:14.675432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:26.174115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:37.833415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:47.990348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:23.339428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:09.832272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:35.571653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:46.488752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:57.577190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:08.592118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:19.560323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:30.428334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:41.403118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:52.368956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:02.805124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:14.926819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:26.445774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:38.245208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:48.241090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:24.072842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:14.233844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:35.956434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:46.745479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:57.833523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:08.837066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:19.897138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:30.712696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:41.657254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:52.764735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:03.039665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:15.189747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:26.726771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:38.487568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:48.694971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:25.178689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:19.361335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:36.359643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:47.019861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:58.096785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:09.093392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:20.265866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:30.982618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:41.938931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:53.135040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:03.319485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:15.454717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:27.006338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:38.761219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:49.022501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:03:25.933187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:23.232962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:36.757893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:47.268622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:04:58.340177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:09.325295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:20.630118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:31.235245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:42.189593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:05:53.427463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:03.692235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:15.719322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:27.274953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T18:06:39.016379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-21T18:07:05.396764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
specialization_idcity_idfc_pricefirst_class_profileseligible_slotspremiumfirst_class_goalavailable_slotsmedian_active_monthstraffic_xbookable_hours_monthactive_calendarsavg_priceadmin_bookingsuser_bookingscountry_codeis_listedfirst_class_limit
specialization_id1.0000.0400.1830.1470.007-0.0150.045-0.0610.0820.1860.0620.0720.1130.0850.0950.5450.3270.545
city_id0.0401.0000.0650.075-0.010-0.056-0.030-0.0810.0500.0440.0360.0510.0620.006-0.0380.8590.4500.883
fc_price0.1830.0651.0000.3210.036-0.0370.093-0.1480.2110.1820.1600.0580.5870.1380.1190.4270.0890.200
first_class_profiles0.1470.0750.3211.0000.4310.3380.599-0.0060.0880.4450.4800.5070.2670.4380.4170.0750.4540.048
eligible_slots0.007-0.0100.0360.4311.0000.9350.7820.669-0.0390.4760.6330.7340.1320.5520.5980.0560.1540.117
premium-0.015-0.056-0.0370.3380.9351.0000.8120.769-0.0430.4670.5560.6730.0450.5320.6010.0570.1300.112
first_class_goal0.045-0.0300.0930.5990.7820.8121.0000.6880.0230.4390.4970.5910.0770.4720.5460.1540.3380.354
available_slots-0.061-0.081-0.148-0.0060.6690.7690.6881.000-0.0770.2320.3010.407-0.1270.2820.3880.1530.1610.243
median_active_months0.0820.0500.2110.088-0.039-0.0430.023-0.0771.0000.112-0.018-0.0250.0780.0540.0100.2110.4410.299
traffic_x0.1860.0440.1820.4450.4760.4670.4390.2320.1121.0000.3410.4990.1960.4710.6300.0910.1750.133
bookable_hours_month0.0620.0360.1600.4800.6330.5560.4970.301-0.0180.3411.0000.8840.4450.7200.5820.0130.1070.000
active_calendars0.0720.0510.0580.5070.7340.6730.5910.407-0.0250.4990.8841.0000.3570.7160.7250.0330.1520.083
avg_price0.1130.0620.5870.2670.1320.0450.077-0.1270.0780.1960.4450.3571.0000.3540.3340.4310.0640.237
admin_bookings0.0850.0060.1380.4380.5520.5320.4720.2820.0540.4710.7200.7160.3541.0000.6890.0290.0630.029
user_bookings0.095-0.0380.1190.4170.5980.6010.5460.3880.0100.6300.5820.7250.3340.6891.0000.0870.1070.103
country_code0.5450.8590.4270.0750.0560.0570.1540.1530.2110.0910.0130.0330.4310.0290.0871.0000.3890.999
is_listed0.3270.4500.0890.4540.1540.1300.3380.1610.4410.1750.1070.1520.0640.0630.1070.3891.0000.089
first_class_limit0.5450.8830.2000.0480.1170.1120.3540.2430.2990.1330.0000.0830.2370.0290.1030.9990.0891.000

Missing values

2023-09-21T18:06:49.687598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-21T18:06:50.702992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

country_codespecialization_idcity_idspecializationspecialization_encityfc_priceis_listedfirst_class_profileseligible_slotspremiumfirst_class_limitfirst_class_goalavailable_slotsmedian_active_monthstraffic_xbookable_hours_monthactive_calendarsavg_priceadmin_bookingsuser_bookings
0ar721100UrólogourologistNeuquén Capital2499.0TRUE1.01.01.010.01.00.08.0408.08.02.00.00.037.0
1ar161042Médico clínicoclinicianMendoza Capital9990.0TRUE1.01.01.010.01.00.051.0227.075.03.00.0109.063.0
2ar62479PsicólogopsychologistVicente López2499.0TRUE1.01.03.010.01.00.00.0126.0122.02.00.0196.022.0
3ar451042NeurocirujanoneurosurgeonMendoza Capital9990.0TRUE1.00.01.010.01.00.053.0126.00.00.00.00.00.0
4ar621100PsicólogopsychologistNeuquén Capital4999.0TRUE1.01.03.010.01.00.049.0957.03.01.03250.0101.00.0
5ar651080PsiquiatrapsychiatristOberá12990.0TRUE1.00.01.010.01.00.053.021.00.00.00.067.00.0
6ar6235PsicólogopsychologistBahía Blanca9990.0TRUE2.04.05.010.02.00.028.0864.0160.04.00.065.046.0
7ar551522Traumatólogoorthopaedic surgeonSan Miguel de Tucumán9990.0TRUE1.02.02.010.01.00.053.0617.086.02.00.00.049.0
8ar72262UrólogourologistLanús Oeste2499.0FALSE1.01.01.010.01.00.07.02.017.03.0875.04.025.0
9ar481522NutricionistanutritionistSan Miguel de Tucumán9990.0TRUE1.02.02.010.01.00.040.0143.0122.02.033.013.030.0
country_codespecialization_idcity_idspecializationspecialization_encityfc_priceis_listedfirst_class_profileseligible_slotspremiumfirst_class_limitfirst_class_goalavailable_slotsmedian_active_monthstraffic_xbookable_hours_monthactive_calendarsavg_priceadmin_bookingsuser_bookings
4771pl1022843lekarz wykonujący zabiegi medycyny estetycznejspecialist in aesthetic medicineRudy199.0FALSE1.01.01.015.01.00.04.00.00.00.00.00.00.0
4772pl102606lekarz wykonujący zabiegi medycyny estetycznejspecialist in aesthetic medicineKościerzyna199.0FALSE1.01.01.015.01.00.02.012.00.00.00.00.00.0
4773pl93248psychologpsychologistDąbrowa Górnicza299.0TRUE2.09.010.015.02.00.015.01186.0123.08.0184.0804.079.0
4774pl1341688protetyk stomatologicznyprosthetistWrocław199.0FALSE1.01.01.015.01.00.06.0412.00.00.00.00.01.0
4775pl152236psychoterapeutapsychotherapistCzłuchów199.0FALSE1.02.02.015.01.00.00.014.018.02.0150.046.013.0
4776pl841710fizjoterapeutaphysiotherapistZabrze299.0TRUE1.04.04.015.01.00.038.0314.0297.06.0148.0301.0249.0
4777pl611710internistainternistZabrze199.0FALSE0.08.015.015.01.01.0NaN76.020.04.0181.09.014.0
4778pl1031250stomatologdentistRuda Śląska299.0TRUE1.013.017.015.01.00.069.0559.0377.011.0333.0937.0259.0
4779pl1841710fizjoterapeuta dziecięcypediatric physiotherapistZabrze199.0FALSE1.01.01.015.01.00.09.038.00.00.00.00.00.0
4780pl811640psychiatrapsychiatristWieliczka299.0TRUE1.01.01.015.01.00.046.0168.00.00.00.00.00.0